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Related Experiment Video

Updated: Jul 3, 2026

In Situ Characterization of Shewanella oneidensis MR1 Biofilms by SALVI and ToF-SIMS

In Situ Characterization of Shewanella oneidensis MR1 Biofilms by SALVI and ToF-SIMS

Published on: August 18, 2017

Validating annotations for uncharacterized proteins in Shewanella oneidensis.

Brenton Louie¹, Peter Tarczy-Hornoch, Roger Higdon

¹Department of Medical Education and Biomedical Informatics, Division of Biomedical and Health Informatics, University of Washington, Seattle, Washington 98101-1304, USA.

Omics : a Journal of Integrative Biology

|August 9, 2008

Summary

This summary is machine-generated.

Annotation databases accurately predict functions for uncharacterized proteins when experimental evidence exists. This study evaluated database performance, finding high accuracy and improved function prediction for proteins of unknown function.

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In Situ Characterization of Shewanella oneidensis MR1 Biofilms by SALVI and ToF-SIMS

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Published on: August 18, 2017

Electrochemical Detection of Deuterium Kinetic Isotope Effect on Extracellular Electron Transport in Shewanella oneidensis MR-1

Electrochemical Detection of Deuterium Kinetic Isotope Effect on Extracellular Electron Transport in Shewanella oneidensis MR-1

Published on: April 16, 2018

Area of Science:

Molecular Biology
Bioinformatics
Proteomics

Background:

Uncharacterized proteins hinder molecular biology understanding.
Current function prediction methods rely on similarity searches but lack validated performance on unknown proteins.
Accurate functional annotation is crucial for biological discovery.

Purpose of the Study:

To evaluate the performance of commonly used annotation databases for predicting functions of uncharacterized proteins.
To develop a benchmark dataset for assessing annotation accuracy.
To determine the reliability of current annotation tools.

Main Methods:

Created a benchmark dataset of 30 previously uncharacterized proteins from Shewanella oneidensis with newly assigned functions.
Evaluated multiple annotation databases using this dataset.
Developed and applied 'conditional accuracy' as an evaluation metric.

Main Results:

Six annotation databases accurately predicted functions for at least 60% of the proteins.
Two databases achieved 90% conditional accuracy.
At least one database correctly predicted the function for 27 out of 30 proteins.

Conclusions:

Annotation databases effectively incorporate new experimental evidence.
These databases demonstrate accuracy in predicting functions for uncharacterized proteins.
Reliable function prediction is achievable when experimental evidence is available.